Recursive Feature Elimination for Machine Learning-based Landslide Prediction Models

This paper proposes a landslide prediction model which uses the recursive feature elimination method. which is one of the key feature selection methods in machine learning that s not tested yet for landslide prediction related applications. The model is tested with the landslide inventories of two landslide-prone areas. The results show that the proposed model achieves an average accuracy of 91.15% and a sensitivity of 83.4% predicting the possibility for a landslide. The findings of this research paper imply that recursive feature elimination can also he effective') used in landslide predictions since it achieves high accuracy.